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Found 6374 publications
Mechanics of the golf lip out
Publication Name: Royal Society Open Science
Publication Date: 2025-11-05
Volume: 12
Issue: 11
Page Range: Unknown
Description:
Sometimes, when a golfer attempts to putt a golf ball, it appears to enter the hole, only to re‑emerge almost immediately, hav‑ ing undergone an angle of turn around the hole rim that can exceed 180◦. We consider the problem from the point of view of mechanics. We show analytically that there are at least two distinct types of lip out: the rim lip out, where the centre of mass of the golf ball does not fall below the level of the green, and the hole lip out where it does. At the heart of both lip outs is a family of degenerate saddle equilibria of the dynamics on the rim (the golf balls of death). When perturbed one way, the golf ball executes a rim lip out. When perturbed another way, the golf ball enters the hole, only to re‑emerge (provided it does not touch the base of the hole) if it is spinning about an axis perpendicular to the wall of the hole.
Open Access: Yes
DOI: 10.1098/rsos.250907
Enhancing lake water level forecasting with attention-based LSTM: a data-driven approach to hydrology and tourism dynamics
Publication Name: Ain Shams Engineering Journal
Publication Date: 2025-11-01
Volume: 16
Issue: 11
Page Range: Unknown
Description:
In recent decades, freshwater lakes in the Northern Hemisphere have faced significant challenges, including severe water shortages and increased stormwater discharges. As a result, accurate forecasting of lake water levels has become essential for effective water resource management, flood mitigation, and ecological sustainability—all of which are interconnected with dynamics in tourism within freshwater basins. This study evaluates the performance of an Attention-based Long Short-Term Memory (LSTM) model compared to a standard LSTM for predicting lake water levels over 5-day and 30-day intervals, utilizing five different input combinations at one of Hungary's popular tourist destinations Lake Velence. The results demonstrate that the Attention-based LSTM consistently outperforms the standard LSTM, particularly in long-term forecasting, as it effectively captures relevant temporal dependencies and reduces error accumulation. Additionally, a Pearson correlation analysis was performed to examine the relationship between guest nights and environmental factors, including lake water level, precipitation, temperature, and evapotranspiration. The findings reveal a strong correlation between guest nights and both temperature and evapotranspiration, while the associations with lake water level and precipitation are relatively weak. This indicates that climate conditions, rather than hydrological variations, primarily drive visitor numbers. The study highlights the importance of integrating advanced machine learning models in hydrological forecasting and tourism planning, providing valuable insights for sustainable water management and climate-adaptive tourism strategies.
Open Access: Yes
Development and Application of a Fuzzy-Apriori-Based Algorithmic Model for the Pedagogical Evaluation of Student Background Data and Question Generation
Publication Name: Algorithms
Publication Date: 2025-11-01
Volume: 18
Issue: 11
Page Range: Unknown
Description:
This study presents a fuzzy-Apriori model that analyses student background data, along with end-of-lesson student-generated questions, to identify interpretable rules. After linguistic and semantic preprocessing, questions are represented in a fuzzy form and combined with background and performance variables to generate association rules, including support, confidence, and lift. The dataset includes 202 students, parent reports from 174 families, 5832 student-generated questions, and 510 teacher-generated questions collected in regular lessons in grades 7–8. The model also incorporates a topic-level dynamic updating step that refreshes the rule set over time. The findings indicate descriptive associations between background characteristics, question complexity and alignment, and classroom performance. It is essential to note that this phase explores possibilities rather than providing a validated instructional method. Question coding inevitably involves subjective elements, and while we conducted the study in real classroom settings, we did not perform causal analyses at this stage. The next step will be developing reliability metrics through longitudinal studies across multiple classroom environments. Future work will test whether using these patterns can inform instructional adjustments and support student learning.
Open Access: Yes
DOI: 10.3390/a18110727
Application of Psychoacoustic Metrics in the Noise Assessment of Geared Drives
Publication Name: World Electric Vehicle Journal
Publication Date: 2025-11-01
Volume: 16
Issue: 11
Page Range: Unknown
Description:
Psychoacoustic metrics offer a valuable complement to traditional noise evaluation methods for gear transmissions, as they account for the human perception of sound quality rather than relying solely on physical measurements. While parameters such as overall sound pressure level (SPL) and spectral content quantify noise intensity and frequency distribution, they often fail to reflect subjective annoyance caused by tonal or high-frequency components common in gear systems. This review provides a structured overview of how psychoacoustic metrics—including loudness, sharpness, roughness, fluctuation strength, and tonality—are applied in the analysis of gear transmission noise. Relevant studies were identified through a comprehensive search across multiple scientific databases, with 54 meeting the inclusion criteria. The findings highlight both the benefits and limitations of these metrics, and present examples of their industrial application in automotive and mechanical engineering contexts. The review also identifies gaps in current research, particularly in integrating psychoacoustic evaluation with predictive modelling and machine learning, and suggests directions for future work.
Open Access: Yes
DOI: 10.3390/wevj16110611
A Study of the Linguistic Landscape of a Hungarian University That Is Going International
Publication Name: Education Sciences
Publication Date: 2025-11-01
Volume: 15
Issue: 11
Page Range: Unknown
Description:
The study of the linguistic landscape is a key area for mapping the linguistic and cultural characteristics of university campuses. This attention is manifest in the language choice employed in the signage on campus, which serves as a physical indicator of these institutions’ linguistic policies and practices. The following paper will present a multi-faculty study conducted at Széchenyi István University in Hungary. The objective of this research is to address the question of how internationalization is explicitly manifested in the institution. A further aim of this investigation was to determine to what extent foreign languages, especially English and German, are represented in the texts found at the university, and what functions these texts perform. Therefore, mixed-method research was conducted in the university’s central academic buildings and their immediate surroundings, during which photos of the signage were taken, analysed, and systematically categorized. This research yielded a comprehensive understanding of the university’s linguistic landscape and revealed the lack of a coherent foreign language policy at the university. The results can provide relevant information for consciously (re)designing the linguistic landscape of the university studied and can help other universities to plan their language policies.
Open Access: Yes
Decision support for sustainable circular food supply chain in Iran: A fuzzy multi-criteria approach
Publication Name: Computers and Industrial Engineering
Publication Date: 2025-11-01
Volume: 209
Issue: Unknown
Page Range: Unknown
Description:
As an interconnected network, the food supply chain links multiple actors across production, processing, distribution and consumption. While it plays a vital role in ensuring food security, safety and economic resilience, the sector also faces growing challenges related to its environmental impact and long-term sustainability. Addressing these issues requires a systemic shift toward sustainable circular supply chain models that support net-zero objectives, decarbonization pathways, and ecosystem-wide coordination. This study aims to explore the key factors influencing sustainable circular supply chain management (SCSCM) across five major sectors of the food industry in Iran: grain, dairy, meat, sugar and carbohydrate products. By incorporating the concept of dynamic capabilities into the supply chain context, this study underscores the importance of organizational adaptability and innovation in facilitating the transition toward circular, low-emission supply chains. A snowball-based literature review revealed a lack of prioritization frameworks tailored to the food industry in Iran. To address this gap, the fuzzy Delphi method (FDM) was used to identify critical factors, followed by the fuzzy analytic network process (FANP) to evaluate and rank them based on expert judgment. The findings indicate that supplier facilities, trade credit, supplier risk management, environmental policy and environmental costs are the five most critical enablers of circular and sustainable transformation within the food supply chain. These identified factors offer a foundation for policymakers and industry leaders to design long-term, ecosystem-oriented strategies that enable systemic change and accelerate progress toward net-zero goals within the sector.
Open Access: Yes
Detection of Harmonic and Interharmonics Contents in Water Desalination Plants' Distribution System Based on Deep Learning Algorithms
Publication Name: Energy Science and Engineering
Publication Date: 2025-11-01
Volume: 13
Issue: 11
Page Range: 5451-5464
Description:
Water desalination plants are significant consumers of electric power, making them some of the largest energy users in power grids. Their electricity consumption presents an urgent challenge for efficient and sustainable operation, and they are among the most power-quality-threatening customers for utilities. This study presents three distinct strategies to enhance prediction accuracy and extend forecasting horizons, aiming to reduce algorithmic and hardware delays. Additionally, it suggests effective methods to compensate for voltage fluctuations, voltage flicker, and dips arising from desalination plants. The paper also discusses forecasting harmonics and interharmonics in current signals. Furthermore, it integrates the above techniques into a comprehensive computing system, along with an active power filter (APF) scheme, within the Simulink framework. A comparison is drawn between the performance of predictive techniques in an APF and a conventional, non-predictive APF. The proposed data augmentation method successfully increases prediction accuracy. By effectively forecasting upcoming waveforms, it reduces algorithmic and hardware delays. These techniques are designed to address multiple power quality issues simultaneously, including harmonics, interharmonics, flicker, and voltage dips, which often coexist in the spectrum as interharmonics. The suggested approach employs Long Short-Term Memory (LSTM) networks combined with the Jetson TX2 embedded artificial intelligence computer to accelerate machine learning applications. This method has proven effective in predicting and classifying time series data, including harmonics, interharmonics, and raw current signals, achieving 100% accuracy. This eliminates the need for designing specific low-pass filters. The evaluation results for this time-domain deep learning-based technique will be reported in the subsections below. The implementation is conducted in Python using the KERAS deep learning framework and TensorFlow backend, and it is evaluated on a workstation equipped with an Intel i7 processor running at 4.0 GHz and 48 GB of RAM.
Open Access: Yes
DOI: 10.1002/ese3.70251
Development of hybrid optimization approach combined with AI-based techniques for prediction of electrical fields in overhead transmission lines
Publication Name: Journal of Supercomputing
Publication Date: 2025-11-01
Volume: 81
Issue: 16
Page Range: Unknown
Description:
Getting a precise estimate of electric fields around extra-high-voltage (EHV) transmission lines is essential for keeping the public safe, ensuring environmental compliance, and planning infrastructure effectively. Unfortunately, traditional numerical methods often struggle with accuracy and can be slow to converge, which makes them less suitable for large-scale projects. This study introduces a hybrid computational framework that combines the Charge Simulation Method (CSM) with the Firefly Algorithm (FA). This combination helps optimize the number, position, and strength of simulation charges, leading to better modeling accuracy and efficiency. Additionally, we have trained three artificial intelligence (AI) models: Multilayer Perceptron Neural Network (MLPNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Least Squares Support Vector Machine (LS-SVM) on real-world field data to reliably predict electric field values. Notably, LS-SVM is being used in this context for the first time and has shown to outperform the other models in accuracy, generalization, and speed. We evaluated the proposed CSM-FA hybrid model alongside AI predictions using metrics like Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R2), revealing significant improvements over traditional methods. Given the heavy computational demands of the optimization and learning phases, we utilized high-performance computing (HPC) resources for implementation. This work not only advances algorithmic innovation and AI-assisted simulation but also enhances HPC applications, providing a scalable and precise solution for real-time field monitoring and regulatory assessments. The methodology aligns well with the scientific goals of The Journal of Supercomputing and fosters advanced research in intelligent power system modeling.
Open Access: Yes
Empowering circular economy transformation through immersive digital open innovation
Publication Name: Journal of Innovation and Knowledge
Publication Date: 2025-11-01
Volume: 10
Issue: 6
Page Range: Unknown
Description:
Despite broad support for sustainability, the transition from linear to circular economic models remains slow and fragmented across industries. Digital technologies such as the metaverse present new opportunities for enabling circular economy (CE) practices, yet their strategic integration within organizational systems is not fully understood. To address this gap, this study proposes and empirically validates a model that explains how metaverse adoption influences CE implementation through the mediating role of Immersive Knowledge Co-Creation (IKCC), and the moderating effect of Open Eco-Innovation Capability (OEIC). Grounded in the knowledge-based view (KBV), interactive learning theory, and dynamic capabilities theory, the research develops a process-driven and capability-oriented framework to explore the mechanisms and boundary conditions for digital-enabled circular transformation. Empirical data were collected from 220 respondents in Germany's advanced manufacturing sector and analyzed using partial least squares structural equation modeling. The results confirm that IKCC significantly mediates the relationship between metaverse adoption and CE implementation, and that this mediated relationship is stronger in firms with high OEIC. These findings contribute to the growing body of literature on digital innovation in the CE by highlighting the critical role of immersive collaboration and organizational readiness in driving circular processes.
Open Access: Yes
